Dual Discriminator GAN: Restoring Ancient Yi Characters

Abstract

In China, the damage of ancient Yi books are serious. Due to the lack of ancient Yi experts, the repairation of ancient Yi books is progressing very slowly. The artificial intelligence is successful in the field of image and text, so it is feasible for the automatic restoration of ancient books. In this article, a generative adversarial networks with dual discriminator (DDGAN) is designed to restore incomplete characters in the ancient Yi literature. The DDGAN integrates the deep convolution generative adversarial network with an ancient Yi comparison discriminator. Through two training stages, it could iteratively optimizes the ancient Yi character generation networks to obtain the text generator According to the loss of comparison discriminator, DDGAN mode could be optimized. The DDGAN model can generate characters to restore the missing stroke in the ancient Yi. The experiment shows that the proposed method achieves a restoration rate of 77.3% when no more than one third of the characters are missing. This work is effective for the protection of Yi ancient books.